Bases: MessagePassing
The edge convolutional operator from the “Dynamic Graph CNN for Learning on Point Clouds” paper.
\[\mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i)} h_{\mathbf{\Theta}}(\mathbf{x}_i \, \Vert \, \mathbf{x}_j - \mathbf{x}_i),\]
where \(h_{\mathbf{\Theta}}\) denotes a neural network, .i.e. a MLP.
nn (torch.nn.Module) – A neural network \(h_{\mathbf{\Theta}}\) that maps pair-wise concatenated node features x of shape [-1, 2 * in_channels] to shape [-1, out_channels], e.g., defined by torch.nn.Sequential.
aggr (str, optional) – The aggregation scheme to use ("add", "mean", "max"). (default: "max")
**kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.
input: node features \((|\mathcal{V}|, F_{in})\) or \(((|\mathcal{V}|, F_{in}), (|\mathcal{V}|, F_{in}))\) if bipartite, edge indices \((2, |\mathcal{E}|)\)
output: node features \((|\mathcal{V}|, F_{out})\) or \((|\mathcal{V}_t|, F_{out})\) if bipartite
Runs the forward pass of the module.
Resets all learnable parameters of the module.
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